Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.
Evaluation of serum diagnosis of pancreatic cancer by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.
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DOI：
10.3892/ijmm.2012.1113
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发表时间：
2012-11
影响因子：
5.4
通讯作者：
Hongjun Gao;Zhaoxu Zheng;Zhigang Yue;Fang Liu;Lanping Zhou;Xiaohang Zhao
中科院分区：
文献类型：
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作者：
Hongjun Gao;Zhaoxu Zheng;Zhigang Yue;Fang Liu;Lanping Zhou;Xiaohang Zhao
蛋白质组学方法已广泛应用于疾病标志物发现研究。本研究的目的是利用表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）发现胰腺癌（PCa）的潜在生物标志物。使用SELDI分析了132例PCa患者和67例健康对照者的粗血清样本。使用支持向量机（SVM）对光谱进行分析，生成基于PCa患者与训练队列中hcc患者之间最大差异表达的蛋白质的预测算法。在测试队列中使用留一交叉验证对该算法进行了测试。根据训练队列中的4个显著峰值，开发了一种区分PCa和hcc患者的分类器。该分类器受到挑战，所有样本在训练队列中达到96.67%的灵敏度和100%的特异性，在测试队列中达到93.1%的灵敏度和78.57%的特异性。此外，分类器正确分类了12/12例Ia期和13/16例IIa期PCa病例。SELDI组合和CA19-9在区分健康人与PCa个体方面优于单独使用CA19-9。这些结果表明，高通量蛋白质组学分析有能力为前列腺癌的早期检测和诊断提供新的生物标志物。
Proteomic methods have been widely used in disease marker discovery research. The aim of this study was to discover potential biomarkers for pancreatic cancer (PCa) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Crude serum samples from 132 patients with PCa and 67 healthy controls (HCs) were analyzed in duplicate using SELDI. Support vector machine (SVM) analysis of the spectra was used to generate a predictive algorithm based on proteins that were maximally differentially expressed between patients with PCa and the HCs in the training cohort. This algorithm was tested using leave-one-out cross-validation in the test cohort. From the 4 significant peaks in the training cohort, a classifier for separating patients with PCa from HCs was developed. The classifier was challenged with all samples achieving 96.67% sensitivity and 100% specificity in the training cohort and 93.1% sensitivity and 78.57% specificity in the test cohort. Additionally, the classifier correctly classified 12/12 stage Ia and 13/16 stage IIa PCa cases. The combination of the SELDI panel and CA19-9 was superior to CA19-9 alone in distinguishing individuals with PCa from the healthy subject group. These results suggest that high-throughput proteomic profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.